AGBQR: Adaptive Generalized Bayesian Quantile Regression

Implements adaptive generalized Bayesian quantile regression with quantile-specific learning rates, HAC-based calibration, Gibbs posterior simulation, posterior summaries, predictive evaluation, and visualization tools. The package builds on the generalized Bayesian composite quantile regression framework of Hardy and Korobilis (2026) <doi:10.2139/ssrn.6618603> by allowing learning rates to vary across quantile levels. The implementation is designed for empirical work with small and moderate time-series samples where posterior calibration and tail-specific inference are important.

Version:	0.1.0
Imports:	quantreg, MASS, stats
Suggests:	testthat
Published:	2026-06-22
DOI:	10.32614/CRAN.package.AGBQR
Author:	Khder Alakkari [aut, cre]
Maintainer:	Khder Alakkari <khderalakkari1990 at gmail.com>
License:	MIT + file LICENSE
NeedsCompilation:	no
Citation:	AGBQR citation info
CRAN checks:	AGBQR results

Documentation:

Reference manual:

AGBQR.html , AGBQR.pdf

Downloads:

Package source:	AGBQR_0.1.0.tar.gz
Windows binaries:	r-devel: AGBQR_0.1.0.zip, r-release: not available, r-oldrel: AGBQR_0.1.0.zip
macOS binaries:	r-release (arm64): AGBQR_0.1.0.tgz, r-oldrel (arm64): AGBQR_0.1.0.tgz, r-release (x86_64): AGBQR_0.1.0.tgz, r-oldrel (x86_64): AGBQR_0.1.0.tgz

Linking:

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